Random Forest, also known as Random Decision Forests, is an ensemble learning method used for classification, regression, and other tasks in machine learning
. It operates by constructing multiple decision trees at training and combining their outputs to reach a single result
. The main features of Random Forest include:
- Flexibility : Random Forest can be used for both classification and regression tasks
- Simplicity : It is easy to use and does not require hyper-parameter tuning, making it a popular choice for many machine learning applications
- Ensemble Method : Random Forest is an extension of the bagging method, as it utilizes both bagging and feature randomness (also known as feature bagging or the random subspace method)
- Bootstrap Aggregating : Each decision tree in the Random Forest is trained on a different subset of examples, which helps to reduce variance within a noisy dataset
Random Forest algorithms have three main hyperparameters: node size, the number of trees, and the number of features sampled
. The Random Forest classifier can be used to solve for regression or classification problems, and it is commonly used in various fields such as finance, healthcare, and e-commerce
